added more examples and better description

app.py

@@ -412,7 +412,9 @@ def search_and_retrieve(
     query_footprint = None
     if query_footprint_str:
         try:
+            print(query_footprint_str)
             query_footprint = ast.literal_eval(query_footprint_str)
+            
             query_footprint = [list(coord) for coord in query_footprint]
         except (ValueError, SyntaxError):
             logging.warning("Could not parse query footprint string.")
@@ -569,6 +571,7 @@ def search_and_retrieve(
 
 if __name__ == "__main__":
     example_list = []
+    google_examples = []
     queries_folder = Path("./data/queries")
     if queries_folder.exists() and queries_folder.is_dir():
         image_extensions = ["*.jpg", "*.jpeg", "*.png"]
@@ -593,6 +596,19 @@ if __name__ == "__main__":
     else:
         logging.warning(f"Examples folder not found: {queries_folder}")
 
+    google_folder = Path("./data/google_maps_queries")
+    if google_folder.exists() and google_folder.is_dir():
+        image_extensions = ["*.jpg", "*.jpeg", "*.png"]
+        google_files = []
+        for ext in image_extensions:
+            google_files.extend(google_folder.glob(ext))
+        if google_files:
+            num_google = min(10, len(google_files))
+            google_examples = [
+                [str(p), str(p.stem).split("_")[0]]  # Empty footprint for Google Maps
+                for p in random.sample(google_files, num_google)
+            ]
+
     model_description = """
     ## Model Details
     This is a public API for inference of the EarthLoc2 model, which implements the amazing works of:
@@ -601,7 +617,7 @@ if __name__ == "__main__":
     - AstroLoc (https://astro-loc.github.io/)
 
     ### Architecture
-    - DINOv2 base with SALAD aggregator out dim = 3084
+    - DINOv2 base with SALAD aggregator out dim = 3072
     - FAISS index ~ 8gb, indexes 161496 * 4 images (4 rotated versions) from 2021
 
     ### Training
@@ -612,8 +628,7 @@ if __name__ == "__main__":
     ### Performance
     - Achieves R@10 = 90.6 on the original EarthLoc test and val sets (when retrieving against whole db as is)
     - Overall performance is around 10% worse than AstroLoc (https://9d214e4bc329a5c3f9.gradio.live/)
-    - Particularly not working well on very small or very large areas. 
-
+    - Works well on satelite images between 1000 sq.km and 50000 sq.km, smaller or higher areas will not produce good results. 
     ### Matching
     - Uses the Xfeat_steerers matcher with 2048 maximal number of keypoints, we recommend Master with 2048 if you have access to GPU (we are too poor for it). 
     """
@@ -633,7 +648,7 @@ if __name__ == "__main__":
             with gr.Column(scale=2):
                 image_input = gr.Image(
                     type="pil",
-                    label="Aerial photos of Earth",
+                    label="Aerial Photos of Earth",
                     height=400,
                 )
                 hidden_footprint_text = gr.Textbox(
@@ -654,13 +669,26 @@ if __name__ == "__main__":
 
                 submit_btn = gr.Button("Localize Image", variant="primary")
 
-                gr.Examples(
-                    examples=example_list,
-                    inputs=[image_input, hidden_footprint_text],
-                    label="Example Queries",
-                    examples_per_page=10,
-                    cache_examples=False,
-                )
+                with gr.Row():
+                    with gr.Column():
+                        if example_list:
+                            gr.Markdown("### ISS Example Queries")
+                            gr.Examples(
+                                examples=example_list,
+                                inputs=[image_input, hidden_footprint_text],
+                                examples_per_page=5,
+                                cache_examples=False,
+                            )
+                    
+                    with gr.Column():
+                        if google_examples:
+                            gr.Markdown("### Google Maps Example Queries")
+                            gr.Examples(
+                                examples=google_examples,
+                                inputs=[image_input, hidden_footprint_text],
+                                examples_per_page=5,
+                                cache_examples=False,
+                            )
 
             with gr.Column(scale=2):
                 map_output = gr.HTML(label="Final Footprint Map")